Face Recognition Method Based On Sparse Subspace Clustering

Face recognition in the field of image processing is an important research hotspot,face recognition technology is widely used in pattern recognition,artificial intelligence,computer vision,etc.he face recognition technology based on sparse representation,sparse subspace clustering is a hot spot in the subspace clustering in recent years,It is a kind of subspace clustering method based on spectral clustering and its basic idea is: high dimensional data space is located in multiple low-dimensional subspace,and can be low-dimensional subspace linear said.Sparse subspace clustering,the basic method is: a group of high-dimensional data for a given subspace representation model is set up,it is concluded that the data in low-dimensional subspace sparse representation of the coefficient,and then according to the similarity coefficient of structure matrix,and applied to spectral clustering algorithm,and clustering of data.Based on sparse representation theory,this paper first expounds a kind of traditional face recognition based on sparse representation,this paper further do to sparse representation algorithm improvement,a sparse representation of the iterative weighted algorithm is proposed.terative weighted sparse subspace clustering algorithm.In order to cluster data,sparse subspace clustering algorithm clusters high-dimensional data to different subspaces by solving minimization algorithm and applying spectral clustering.Iterative algorithm has more fair punishment value then the traditional algorithm,with balancing the influence of magnitude of data.The algorithm is applied to the sparse subspace clustering to improve the traditional sparse subspace clustering performance for data.Simulation experiment recognizing and classify Yale B face data image.The clustering effect is very good,proving the superiority of the improved algorithm.We offer two kinds of sparse subspace clustering optimization algorithm,sparse linear space clustering and sparse affine subspace clustering,based on the existing theory of sparse subspace clustering algorithm.For different data gathering,these two kinds of optimization algorithm has different clustering results.In this paper,we obtain different sparse coefficientmatrix by sparse expression.In order to achieve cluster,the sparse coefficient matrix is applied to relatively simple regularization of spectral clustering algorithm.Application of Yale B data,we recognize and classify face image : using sparse linear space clustering algorithm is better than the sparse affine subspace clustering algorithm;Comparing with the traditional sparse subspace clustering,it is more fast and efficient in the time of execution and error rate of algorithm.